Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring
Abstract Background Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events in clinical settings using a flexible chest patch with tri-axial accel...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-025-02879-y |
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| _version_ | 1832571603725058048 |
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| author | Jiawen Wang Chunyan Min Feng Yu Kai Chen Ling Mao |
| author_facet | Jiawen Wang Chunyan Min Feng Yu Kai Chen Ling Mao |
| author_sort | Jiawen Wang |
| collection | DOAJ |
| description | Abstract Background Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events in clinical settings using a flexible chest patch with tri-axial acceleration sensors. Methods Twenty-five young healthy persons (hereinafter referred to as healthy adults) and twenty-five clinically diagnosed pneumoconiosis patients (hereinafter referred to as patients) participated in the experiment by wearing a flexible chest patch with an embedded ACC sensor. The top 56% of the highest scoring features were then combined using several feature selection algorithms to perform the cough classification task. The multicriteria decision making (MCDM) method was used to select the classifier with the highest scores. Results The optimized classifier proposed in this paper achieved an accuracy of 87.1%, precision of 95%, recall of 79.1%, F1 score of 86.4%, and AUC of 95.4% for recognizing coughs in healthy adults; an accuracy of 96.1%, precision of 95%, recall of 97.4%, F1 score of 96.2%, and AUC of 98.7% for recognizing coughs in patients; and an overall accuracy of 92% for distinguishing coughs in the combined group of healthy adults and patients. Conclusions Our study demonstrated the effectiveness of an automated cough recognition system in both pneumoconiosis patients and healthy adults. This approach facilitates daily remote monitoring of cough occurrence in individuals with pneumoconiosis, potentially enhancing the ability of physicians to evaluate clinical status. |
| format | Article |
| id | doaj-art-99ff93f4e5b044649dee50850715daec |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-99ff93f4e5b044649dee50850715daec2025-02-02T12:27:51ZengBMCBMC Medical Informatics and Decision Making1472-69472025-01-0125111110.1186/s12911-025-02879-yCough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoringJiawen Wang0Chunyan Min1Feng Yu2Kai Chen3Ling Mao4School of Mechanical Engineering, Hangzhou Dianzi UniversityDepartment of Pneumoconiosis, Shanghai Pulmonary Hospital, Tongji UniversitySchool of Mechanical Engineering, Hangzhou Dianzi UniversitySchool of Mechanical Engineering, Hangzhou Dianzi UniversityNHC Key Laboratory of PneumoconiosisAbstract Background Many respiratory diseases such as pneumoconiosis require to close monitor the symptoms such as abnormal respiration and cough. This study introduces an automated, nonintrusive method for detecting cough events in clinical settings using a flexible chest patch with tri-axial acceleration sensors. Methods Twenty-five young healthy persons (hereinafter referred to as healthy adults) and twenty-five clinically diagnosed pneumoconiosis patients (hereinafter referred to as patients) participated in the experiment by wearing a flexible chest patch with an embedded ACC sensor. The top 56% of the highest scoring features were then combined using several feature selection algorithms to perform the cough classification task. The multicriteria decision making (MCDM) method was used to select the classifier with the highest scores. Results The optimized classifier proposed in this paper achieved an accuracy of 87.1%, precision of 95%, recall of 79.1%, F1 score of 86.4%, and AUC of 95.4% for recognizing coughs in healthy adults; an accuracy of 96.1%, precision of 95%, recall of 97.4%, F1 score of 96.2%, and AUC of 98.7% for recognizing coughs in patients; and an overall accuracy of 92% for distinguishing coughs in the combined group of healthy adults and patients. Conclusions Our study demonstrated the effectiveness of an automated cough recognition system in both pneumoconiosis patients and healthy adults. This approach facilitates daily remote monitoring of cough occurrence in individuals with pneumoconiosis, potentially enhancing the ability of physicians to evaluate clinical status.https://doi.org/10.1186/s12911-025-02879-yCough recognitionPneumoconiosisTri-axial accelerometer (ACC)MCDM |
| spellingShingle | Jiawen Wang Chunyan Min Feng Yu Kai Chen Ling Mao Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring BMC Medical Informatics and Decision Making Cough recognition Pneumoconiosis Tri-axial accelerometer (ACC) MCDM |
| title | Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring |
| title_full | Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring |
| title_fullStr | Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring |
| title_full_unstemmed | Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring |
| title_short | Cough recognition in pneumoconiosis patients based on a flexible patch with an embedded ACC sensor for remote monitoring |
| title_sort | cough recognition in pneumoconiosis patients based on a flexible patch with an embedded acc sensor for remote monitoring |
| topic | Cough recognition Pneumoconiosis Tri-axial accelerometer (ACC) MCDM |
| url | https://doi.org/10.1186/s12911-025-02879-y |
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